A. If a rifleman's gunsight is adjusted correctly, but he...

a. If a rifleman's gunsight is adjusted correctly, but he has shaky arms, the bullets might be scattered widely around the bull's-eye target. Draw a sketch of the target with the bullet holes. Does this show variation (lack of precision) or bias? b. Draw a second sketch of the target if the shots are unbiased and have precision (little variation). The rifleman's aim is not perfect, so your sketches should show more than one bullet hole.

a. If a rifleman's gunsight is adjusted correctly, but he has shaky arms, the bullets might be scattered widely around the bull's-eye target. Draw a sketch of the target with the bullet holes. Does this show variation (lack of precision) or bias?
b. Draw a second sketch of the target if the shots are unbiased and have precision (little variation).
The rifleman's aim is not perfect, so your sketches should show more than one bullet hole.

Key Concepts

Accuracy

Accuracy refers to how close the average of repeated measurements is to the true or accepted value. A system can be accurate if the measurements are both precise (low variation) and unbiased (little or no systematic error). It is the overall closeness of the experiments to the target that determines accuracy.

Precision

Precision describes the degree of reproducibility or consistency of repeated measurements. When measurements are highly precise, they tend to cluster closely together, indicating low variation. Precision is important because it reflects the reliability of the measurement process, even if the measurements might be consistently offset due to bias.

Variation

Variation refers to the random fluctuation or scatter observed in a series of measurements. It is the extent to which individual samples differ from each other due to random errors or other uncontrolled factors. In experimental data, a high amount of variation means that repeated measurements do not consistently provide the same result, which can reduce confidence in the precision of the measurement process.

Bias

Bias is a systematic error that causes measurements to deviate from the true or expected value in a consistent direction. Unlike variation, bias indicates a fixed error source that shifts the average of the measurements away from the target value. Recognizing bias is crucial for identifying and correcting underlying flaws in a measurement system or experimental design.

Transcript

Please give Ace some feedback